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- Boberg, Julia, et al.
(författare)
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Swedish multimodal cohort of patients with anxiety or depression treated with internet-delivered psychotherapy (MULTI-PSYCH)
- 2023
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Ingår i: BMJ Open. - : BMJ Publishing Group Ltd. - 2044-6055. ; 13:10
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Tidskriftsartikel (refereegranskat)abstract
- Purpose Depression and anxiety afflict millions worldwide causing considerable disability. MULTI-PSYCH is a longitudinal cohort of genotyped and phenotyped individuals with depression or anxiety disorders who have undergone highly structured internet-based cognitive-behaviour therapy (ICBT). The overarching purpose of MULTI-PSYCH is to improve risk stratification, outcome prediction and secondary preventive interventions. MULTI-PSYCH is a precision medicine initiative that combines clinical, genetic and nationwide register data.Participants MULTI-PSYCH includes 2668 clinically well-characterised adults with major depressive disorder (MDD) (n=1300), social anxiety disorder (n=640) or panic disorder (n=728) assessed before, during and after 12 weeks of ICBT at the internet psychiatry clinic in Stockholm, Sweden. All patients have been blood sampled and genotyped. Clinical and genetic data have been linked to several Swedish registers containing a wide range of variables from patient birth up to 10 years after the end of ICBT. These variable types include perinatal complications, school grades, psychiatric and somatic comorbidity, dispensed medications, medical interventions and diagnoses, healthcare and social benefits, demographics, income and more. Long-term follow-up data will be collected through 2029.Findings to date Initial uses of MULTI-PSYCH include the discovery of an association between PRS for autism spectrum disorder and response to ICBT, the development of a machine learning model for baseline prediction of remission status after ICBT in MDD and data contributions to genome wide association studies for ICBT outcome. Other projects have been launched or are in the planning phase.Future plans The MULTI-PSYCH cohort provides a unique infrastructure to study not only predictors or short-term treatment outcomes, but also longer term medical and socioeconomic outcomes in patients treated with ICBT for depression or anxiety. MULTI-PSYCH is well positioned for research collaboration.
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- Ekman, Urban, et al.
(författare)
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The MemClin project : a prospective multi memory clinics study targeting early stages of cognitive impairment
- 2020
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Ingår i: BMC Geriatrics. - : BMC. - 1471-2318. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- Background: There remains a lack of large-scale clinical studies of cognitive impairment that aim to increase diagnostic and prognostic accuracy as well as validate previous research findings. The MemClin project will amass large quantities of cross-disciplinary data allowing for the construction of robust models to improve diagnostic accuracy, expand our knowledge on differential diagnostics, strengthen longitudinal prognosis, and harmonise examination protocols across centres. The current article describes the Memory Clinic (MemClin) project's study-design, materials and methods, and patient characteristics. In addition, we present preliminary descriptive data from the ongoing data collection.Methods: Nine out of ten memory clinics in the greater Stockholm area, which largely use the same examination methods, are included. The data collection of patients with different stages of cognitive impairment and dementia is coordinated centrally allowing for efficient and secure large-scale database construction. The MemClin project rest directly on the memory clinics examinations with cognitive measures, health parameters, and biomarkers.Results: Currently, the MemClin project has informed consent from 1543 patients. Herein, we present preliminary data from 835 patients with confirmed cognitive diagnosis and neuropsychological test data available. Of those, 239 had dementia, 487 mild cognitive impairment (MCI), and 104 subjective cognitive impairment (SCI). In addition, we present descriptive data on visual ratings of brain atrophy and cerebrospinal fluid markers.Conclusions: Based on our current progress and preliminary data, the MemClin project has a high potential to provide a large-scale database of 1200-1500 new patients annually. This coordinated data collection will allow for the construction of improved diagnostic and prognostic models for neurodegenerative disorders and other cognitive conditions in their naturalistic setting.
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- Leosdottir, M., et al.
(författare)
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Improving smoking cessation after myocardial infarction by systematically implementing evidence-based treatment methods : a prospective observational cohort study
- 2021
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Ingår i: European Heart Journal. - : Oxford University Press. - 0195-668X .- 1522-9645. ; 42:Suppl. 1, s. 1409-1409
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: For smokers who suffer a myocardial infarction (MI), smoking cessation is the most effective measure to reduce recurrent event risk. Still, evidence-based treatment methods for aiding smoking cessation post-MI are underused.Purpose: To compare the odds of smoking cessation at two-months post-MI before and after implementing a set of pre-specified routines for optimization of evidence-based treatment methods for smoking cessation, with start during admission.Methods: Structured routines for early smoking cessation counselling and treatment optimization were implemented at six cardiac rehabilitation (CR) centres in Sweden. The routines included CR nurses providing current smokers hospitalized for acute MI with short consultation, written material, and optimal dosage of nicotine replacement therapy during admission, increasing early prescription of varenicline for eligible patients, and contacting the patients by telephone 3–5 days after discharge, after which usual care CR follow-up commenced. Centres were also encouraged to strive for continuity in nurse-patient care. Patient data was retrieved from the SWEDEHEART registry and medical records. Using logistic regression, we compared the odds for smoking cessation at two-months post-MI for currently smoking patients admitted with MI (a) before (n=188, median age 60 years, 23% females) and (b) after (n=195, median age 60 years, 29% females) routine implementation. Secondary outcomes included adherence to implemented routines and the association of each routine with smoking cessation odds at two-months.Results: In total, 159 (85%) and 179 (92%) of enrolled patients attended the two-month CR follow-up, before and after implementation of the new routines. After implementation, a significantly larger proportion of patients (65% vs 54%) were abstinent from smoking at two-months (crude OR 1.60 [1.04–2.48], p=0.034) (Figure 1). Including only those counselled during admission (n=89), 74% (vs 54%) were abstinent at two-months (crude OR 2.50 [1.42–4.41], p=0.002). After the new routine implementation patients were counselled more frequently during admission (50% vs 6%, p<0.001), prescribed varenicline at discharge or during follow-up (23% vs 7%, p<0.001), and contacted by telephone during the first week post-discharge (18% vs 2%, p<0.001), compared to before implementation. Crude and adjusted associations between each routine and smoking cessation at two-months are shown in Table 1. Entering all routines into the regression model simultaneously, being prescribed varenicline before discharge or during follow-up had the strongest independent association with smoking abstinence at two-months (adjusted OR 4.09 [1.68–10.00], p=0.002).Conclusion: Our results support that readily available methods for aiding smoking cessation can be implemented effectively in routine practice, with possible beneficial effects on smoking cessation for the high-risk group of smoking MI patients.
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- Wallert, J., et al.
(författare)
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Predicting adherence to internet-delivered cognitive behaviour therapy for comorbid symptoms of depression and anxiety after myocardial infarction
- 2018
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Ingår i: European Heart Journal. - : Oxford University Press. - 0195-668X .- 1522-9645. ; 39, s. 1112-1112
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Psychotherapeutic treatment for the subgroup of patients with MI that also suffer from comorbid symptoms of anxiety and/or depression (MI-ANXDEP) is part of cardiac rehabilitation (CR). Adherence to a range of treatments and lifestyle advice is crucial for risk reduction in these patients. Understanding the relative importance of predictors of adherence to internet-delivered cognitive behaviour therapy (ICBT) for MI-ANXDEP could improve their targeted care.Purpose: We estimated the relative importance of a range of established and novel predictors of adherence to ICBT for MI-ANXDEP patients.Method: The study sample consisted of 90 MI-ANXDEP patients (58.4 years, 62% men) recruited from 25 hospitals in Sweden who were randomised to active treatment in the ICBT trial U-CARE Heart. Time-point of prediction was at completion of the first homework assignment (HWA), and adherence was gauged at the end of treatment (48% adherers). Adherence was defined as completing at least the first two HWAs within the 14-week treatment period. A supervised machine learning (ML) procedure, applying 3x10 cross-validated recursive feature elimination with a random forest model as internal classifier, estimated the relative importance of predictors for adherence from a range of patient demographic, clinical, and linguistic variables that were available at completion of the first HWA.Result: Out of 34 potential predictors, ML selected an optimal set of 19 predictors (Accuracy 0.64, 95% CI 0.61–0.68). The strongest predictors for being classified as adherent were in order of relative importance (1) higher self-rated cardiac fear (CAQ fear), (2) female sex, (3) more words used by the patient to answer the first homework assignment (Number of words), (4) higher self-rated general cardiac anxiety (CAQ total), and (5) a higher rate of words used by the patient that were identical with words prompted by the first homework assignment (Number of mutual words), as depicted in the figure.Conclusion(s): It is of clinical importance to understand poor adherence to ICBT treatment in the high risk MI-ANXDEP subpopulation. Higher cardiac anxiety and female sex were the strongest predictors for adherence. A novel finding was that linguistic variables were important for predicting adherence, particularly the number of words used may signify the degree of personal investment and motivation for treatment, and the number of mutual words used may be a proxy for therapeutic alliance within the treatment. Education had no predictive value. Future research should investigate potential causal mechanisms, and whether these findings replicate outside of Sweden, in larger samples, and for similar eHealth treatments.
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| 20. |
- Wallert, John, et al.
(författare)
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Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data
- 2022
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Ingår i: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder {MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18-75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008-2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off <= 10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and {iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness.
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